Leaked: What's in TPP's intellectual property chapter - <AUTHOR> http://www.politico.com/agenda/story/2015/06/tpp-deal-leaked-pharma-000126 <comment><USER2> When the director of policy for Doctors Without Borders tells the public that "U.S. negotiators have basically functioned as drug lobbyists" and "We consider this the worst-ever agreement in terms of access to medicine", there's really little more that needs saying.

I love that the best defense unnamed "U.S. officials" can offer is that "major compromises" will likely still be made. In other words: yes we're acting as drug company lobbyists, but we expect some of these other countries to stand up for their citizens enough to get a few concessions.

Fantastic stuff. "Thanks Obama" (and Hillary)

<comment><USER3> I don't see why his affiliation with Doctors Without Borders carries argument- ending weight here. They do noble work, but their interest is the well-being of people outside the U.S. The best case scenario for them is U.S. companies doing all the expensive R&D, and their patients outside the U.S. getting the resulting drugs for free.

The USTR, in contrast, has the _sole_ responsibility of maximizing the benefit to the U.S. It's in our interest if people outside the U.S. have to pay us a bunch of money to get the drugs we invent.

That is not to say that we shouldn't be giving free drugs to people who need it. But that's a policy decision decidedly outside the scope of the USTR. We have agencies like USAID whose job it is to handle humanitarian aid.

<comment><USER4> I live in New Zealand - we've chosen to have socialised medicine - we buy drugs in bulk, for the whole country - it gives the national drug buying agency lots of market power - it doesn't force drug companies to sell to us under cost - but it does pick and choose hich drugs it buys with its limited budget - if they want our business they have to compete with other companies in front of a knowledgable experienced buyer acting on our behalf.

No one here is getting drugs for free, just at reasonable prices that may not be available in the US

TPP as currently written will neuter this advantage costing us billions that will come out of my taxes

<comment><USER5> Same for us in Australia. Both Australia and New Zealand are getting the spiky end of the stick in regards to pharmaceuticals, we have a similar system of socialised medicine.

<comment><USER6> It's probably very similar for most industrialized countries to some extent. In Netherland (which probably has the most expensive health care in Europe), insurance companies negotiate about the prices, which means those companies decide which medicine is and isn't covered, and while they still have a reasonable amount of negotiating power, it's not as much as that of a more centralized approach. Still better than the US approach, but certainly the weakest of the EU.

</comment></comment></comment></comment></comment><comment><USER7> I, for one, am glad to know that that interests of everyone important are well protected in this treaty.

I just wish I knew how to join this class of people who are important. Apparently being a citizen is not enough anymore.

<comment><USER8> There's been several carefully crafted changes to the language of American politics over the past twenty-five years and "citizen" has been replaced by "taxpayer" [1] as the relevant political constituency. Since corporations ostensibly pay taxes, their personhood has been lifted. Likewise, since wealthy individuals tend to pay more taxes on an absolute basis (despite an increasingly regressive system) their interests within the constituency of taxpayers are also lifted.

[1]: The case for "taxpayers" has been assisted by the language of "victim's rights" wherein people first accepted the idea that some citizens should have higher status before the law than others. Using the idea that paying taxes is a form of theft, taxpayers establish their victimhood and by an extension a privileged interest over ordinary citizens in matters concerning taxation. The obverse of the coin is "consumer rights" wherein buying "Little Plastic Shit" is seen as a high form of social engagement and the primary civic duty of ordinary Americans.

<comment><USER9> I think you are letting the citizenry off the hook for their part in this. The only reason corporations gained this power is because they have more collective money (or at least are more willing to use that money) to lobby. The only reason lobbying works is because politicians need money to get elected. The only reason politicians need money to get elected is because people are so susceptible to political marketing. Coporate personhood is therefore just a symptom of a larger problem, a disinterested and easily manipulated electorate.

<comment><USER10> I, for one, am glad to learn that we can fix all this by voting more often, or better, or whatever. Now that I think back, my junior high civics class taught me the same thing!

<comment><USER9> I am sensing sarcasm, so I just want to point to the 2014 primary turnout numbers. Less than 18% [1] of total eligible voters cast a ballot in the 2014 primary elections. When you have that much apathy, it suddenly becomes a lot easier to sway elections with a few million dollars.

[1] - [http://humanevents.com/2014/10/08/republican-2014-primary- tu...](http://humanevents.com/2014/10/08/republican-2014-primary-turnout-tops- democrats-for-the-second-time-since-1930/)

</comment></comment></comment></comment></comment><comment><USER11> This interview with Assange that democracynow did sheds some light on this 'trade' agreement: [http://www.democracynow.org/2015/5/27/julian_assange_on_the_...](http://www.democracynow.org/2015/5/27/julian_assange_on_the_trans_pacific)

The whole interview is worth watching.

</comment><comment><USER12> In Spam Nation _, the primary reason spam is successful (why people click those weird ads) is because they get working drugs for cheap that in many cases are chemically indifferent to their expensive prescription counterparts.

_ [http://krebsonsecurity.com/tag/spam- nation/](http://krebsonsecurity.com/tag/spam-nation/)

<comment><USER6> So if access to health care for the poor isn't a good enough reason to make drugs cheaper, then surely finally killing spam is?

</comment></comment><comment><USER13> "The Obama administration often describes TPP as the most progressive free- trade deal in history"

Does anyone see stronger government enforced monopolies (patents) as the opposite of free trade?

<comment><USER3> All property is a government enforced monopoly. Without property there is no trade. There is no reason to transact when you can just take or copy.

<comment><USER14> Payment for services.

</comment></comment></comment><comment><USER15> There is a very interesting podcast (LSE series about a year ago now), with the chair of the previous round of _physical goods talks_ explaining what has gone wrong, and how to fix it. In essence the idea is a good one - set out worldwide standards of trade - like WTO - for intellectual goods as well as physical.

If I remember it's things like having same standards of animal welfare innabbatoirs (the U.S. Think EU chickens are dirty IIRC).

But it has gone south because it was played badly politically - the heads of the main negotiating countries should apparently have a summit, announce it was crap and announce a new round that will be

\- public and open \- use a highest standard wins (ie if US rules on X are more stringent than EU then the U.S. Rules are the standard for TIPP) \- something else about timelines - I think not setting big deadlines \- being more multilateral like WTO

It seems a sensible move and likely to take a lot of the sting out of "rules made behind closed doors, race to the bottom etc"

Contrary views welcomed...

<comment><USER16> What about if one country wants to highen their standards in a certain area, will all the countries that signed up then immediately have to follow? Won't the other countries bully the first one to stay to the status quo?

Also, are there really always such a thing as 'highest standard'? Couldn't it be that more often than not, the rules aim for opposing values?

<comment><USER17> > Won't the other countries bully the first one to stay to the status quo?

That is _precisely_ what all of these trade agreements are about, TPP, TiSA, etc..

</comment></comment></comment><comment><USER18> Even The Atlantic which I found one of the better "mass media" sources rejoices for TPP and lists none of the criticism. I am much afraid all this doesn't reach the masses.

</comment><comment><USER19> The article missed some of the other giveaways to Big Pharma. One is a narrowing of the patent obviousness requirement for "me-too" drugs. Claritin (loratidine) is the classic example. Loratidine is a stereoisomer; there's a left handed and a right handed version of the molecule. One has an anti- allergy effect, and the other doesn't seem to do much. (That's very common; biology isn't symmetrical at the molecular level.)

When the patent ran out on Claritin, the manufacturer came out with Clarinex, which contains only the one isomer that does something useful. They then pitched doctors hard to switch their patients to Clarinex, with considerable success.

But the manufacturer lost a patent case, on the grounds that separating the isomers and only keeping the useful one is obvious to anyone skilled in the art.[1] Now that isomer separation is routine, although non-trivial, the FDA requires it as a purification step if the non-useful isomer has any negative effects.[2] You can't claim that a step required by existing regulation is a new invention.

There's a clause in the TPP IP section to reverse that decision.[3] _" [US/JP propose; CL/MY/PE/SG/VN/BN/AU/NZ/CA/MX oppose: For greater certainty, a Party may not deny a patent solely on the basis that the product did not result in an enhanced efficacy of the known product when the applicant has set forth distinguishing features establishing that the invention is new, involves an inventive step, and is capable of industrial application.]"_ That's from last year's draft on Wikileaks; I haven't seen the new draft yet.

Incidentally, the TPP resolves the issue of patentablity for software and business methods: _" Subject to the provisions of paragraph 2 and 3 (which relate mostly to plant and animal patents), each Party shall make patents available for any invention, whether a product or process, in all fields of technology, provided that the invention is new, involves an inventive step, and is capable of industrial application."_ So software and business methods must be made patentable in all TPP-signatory countries. This, again, is from last year's draft. Check the new one once it gets published.

[1] [http://www.prnewswire.com/news-releases/schering-plough- lose...](http://www.prnewswire.com/news-releases/schering-plough-loses-patent- lawsuit-over-claritin-opening-door-for-cheaper-generic-versions-70880857.html) [2] [http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInforma...](http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm122883.htm) [3] [https://wikileaks.org/tpp-ip2/](https://wikileaks.org/tpp-ip2/)

<comment><USER20> Is it really clear that the draft TPP language quoted here would reverse the Clarinex decision? It seems to me that the phrase "involves an inventive step" would permit challenges based on obviousness. See for example [0]. While what is "obvious" and what is "inventive" can always be debated, I don't see that this language alters the ground on which that debate will play out.

[0] [https://en.wikipedia.org/wiki/Inventive_step_and_non- obvious...](https://en.wikipedia.org/wiki/Inventive_step_and_non-obviousness)

<comment><USER19> It's intended to allow patents on "me too" drugs generally.[1][2][3]

[1] [http://www.healthline.com/health-news/would-the-trans- partne...](http://www.healthline.com/health-news/would-the-trans-partnership- boost-drug-prices-and-endanger-access-061215#3) [2] [https://www.citizen.org/documents/specific%20provisions%20fi...](https://www.citizen.org/documents/specific%20provisions%20final%20draft%20w.o.pdf) [3] [http://www.nationalobserver.com/2015/06/04/opinion/implicati...](http://www.nationalobserver.com/2015/06/04/opinion/implications- canada-fast-tracked-trans-pacific-partnership)

</comment></comment></comment><comment><USER21> At least they have avoided giving it an Orwellian name including the phrase "free trade". Free trade can be described in a few sentences. These voluminous inter-governmental managed trade agreements are just protectionism by another name.

</comment><comment><USER22> TPP and any similar agreements should be totally opposed until they'll be negotiated in public.

</comment><comment><USER23> Can a progressive democrat please explain to me why their hero is supporting this?

PS To those trying to claim he isn't a progressive... Obama may not be the progressive you want, but he is the one you deserve.

<comment><USER24> Indeed. I generally like Obama (except for Espionage Act prosecutions, continued existence of Guantanamo detention centers, and drone wars), but this one makes no sense.

Can a conservative republican explain why the Republican leadership is supporting this? I can't really understand why free market people would be in favor of state-granted and enforced-at-taxpayer-expense monopolies. Nor do I understand why extreme patriots would be in favor of Investor-State Dispute Systems. The latter seems like a sovereignty giveaway, to an unaccountable international body. In a way, ISDS seems like big government gone wild, but it's an unelected, unaccountable international government, which should offend small government types, too.

<comment><USER25> As a "conservative Republican" since the Nixon era (didn't know why it was important that he beat HHH and George Wallace in '68, knew why it was and still is important that he beat McGovern in '72), I can say in short that we don't support these things.

Why does the Republican leadership support them? At the more reductionist level, I've seen it best described as a "donor riot"; rather obviously the interests of the wealthy who identify as Republican are different that the base (and this is one reason why the Emmanuel Goldsteining of the _libertarian_ Koch brothers strikes us as bizarre, then again they've got some truly dangerous thoughts).

At a more general level, these "establishment" Republicans are, or aspire to be, members of the ruling class. We of the base obviously aren't, and I don't think many of us aspire to be.

"Offend" is a good word, we are mightily offended by all this. But there's only so much we can do short of, say, starting a 3rd party that's successful enough to either threaten the Republican party such that it adopts our positions, or replace it like it replaced the Whigs (or there's a thesis that the Democratic party will die since its war on arithmetic means it will eventually fail to keep its promises, the Republican party will replace it as the party of the state, and the 3rd party becomes the natural opposition). Short of that, we can withdraw our votes, which has a strictly limited effect in the Congress, and we can and have withheld our money, which gets us back to that donor riot.

Ah, I should note there's always been a non-conservative faction in the Republican party. Its abolitionist roots were in opposition to the conservative nature of the Whigs, remember the trusting busting and imperialism of Teddy Roosevelt, and the north-east wing in general is much less conservative. Think of Nelson Rockefeller who opposed conservative Goldwater, not so conservative Nixon, and was Ford's first VP. Or former governor of Massachusetts and "severe conservative" Mitt Romney (hint, that's the first and last time I've every heard someone use the word "severe" like that).

</comment></comment></comment><comment><USER26> Don't even need to read it - the whole secrecy farce surrounding it guarantees that its content is 100% rubber glove exam.

</comment><comment><USER27> Where can I see the leaked document?

<comment><USER28> [http://readthetpp.com](http://readthetpp.com)

</comment></comment><comment><USER29> Going by rank of the top 10 pharma companies [https://en.wikipedia.org/wiki/List_of_pharmaceutical_compani...](https://en.wikipedia.org/wiki/List_of_pharmaceutical_companies) we can see that 4 are American and 6 are European.

The total revenue of that top 10 is 60% European companies and 40% American.

Now if this TTP chapter is two-way and with that equaly so for European markets then if anything would it not favour Europe more. Though between the two collectives involved. This only shuts out generics made outside of those two markets more than currently.

As for doctors without borders, they operate outside this trade agreement location wise and would not that open up for cheaper supplies from the producers outside this trade agreement. I would of thought so.

Though would perhaps, once settle reduce production runs of generics in some area's that depended upon this `sudo` gr[a¦e]y market. Then that may raise production costs, though if mroe than one generic then it would become more a price war without the more fruitful markets to utilise. Though always new markets and myself not sure about medical drugs and Russia and how that works at all.

But really such agreements need to be public before signing as about trade for the people as a whole and not a niche area that impacts national security of the people.

So the whole aspect not public just does not sit well, nor bode well for trust or indeed any scrutinising and that which has and has leaked. Just hard to get the full picture as hard to scale.</comment>


Unsupervised Feature Learning and Deep Learning Tutorial - <AUTHOR> http://deeplearning.stanford.edu/tutorial/ <comment><USER2> For anyone interested, I have been really interested in deep learning and have been using the following resources:

For image processing (CNN) [https://cs231n.github.io/](https://cs231n.github.io/)

For natural language processing (RNN) [http://cs224d.stanford.edu/syllabus.html](http://cs224d.stanford.edu/syllabus.html)

I also found the following coursera helpful [https://www.coursera.org/course/neuralnets](https://www.coursera.org/course/neuralnets) [https://www.coursera.org/learn/machine- learning](https://www.coursera.org/learn/machine-learning)

<comment><USER3> Does anyone know how to access past courses on coursera if you didn't sign up in a timely manner?

<comment><USER4> I think you just sign in and click on a big green button "Go to Course" on the right - at least it worked for me with neuralnets course by Hinton, which is really great.

</comment></comment></comment><comment><USER5> Is there a similar tutorial or resource that shows how to do face detection (not recognition) using deep learning. I came across tutorial from kaggle [1] but I'm looking for something that uses ML to detect where the faces are in an image.

[1] - [http://danielnouri.org/notes/2014/12/17/using- convolutional-...](http://danielnouri.org/notes/2014/12/17/using- convolutional-neural-nets-to-detect-facial-keypoints-tutorial/)

</comment><comment><USER6> > If you are not familiar with these ideas, we suggest you go to this Machine > Learning course and complete sections II, III, IV (up to Logistic > Regression) first.

What is the link to that course?

<comment><USER7> The group is held by Prof. Ng which has a ML course on coursera [https://www.coursera.org/learn/machine- learning](https://www.coursera.org/learn/machine-learning) \- lecture notes of the in class course are here [http://cs229.stanford.edu/materials.html](http://cs229.stanford.edu/materials.html) (but without the lecture it may be.. without the lecture:))

<comment><USER8> Definitely worth it. I had to drop out around week 5 due to a lack of time, but just started a new job that'll take me back to 40-hour weeks (down from 80-100), so I'll give it another shot next time they run the course.

<comment><USER9> The latest session started today, from 5 Oct to 27 Dec.</comment></comment></comment></comment>


All About Skimmers - <AUTHOR> http://krebsonsecurity.com/all-about-skimmers/

<comment><USER2> I've recently gotten into the habit of putting a fingernail under the keypad and applying slight sideways pressure to the card-reader every time I use my card at an ATM or gas pump. There's minimal risk of damaging legitimate machinery (it's not like I apply much pressure), and I figure that it has to cut my chances of being skimmed by a bit.

</comment><comment><USER3> Evil, but ingenious.</comment>


EU regulations on algorithmic decision-making and a "right to explanation" - <AUTHOR> http://arxiv.org/abs/1606.08813 <comment><USER3> Disclaimer - I work in the financial services industry.

This is nothing new in most financial service machine learning algorithms have to meet these requirements. This is why why supervised analytics are more popular, and one of the reason why algorithms such as credit scores are typically generated and structured as scorecards with explicit reason codes for each independent characteristic, variable or vector.

This is really needed for a number of different reasons. The biggest is that algorithms are increasingly running our world. Even outside of mortgage, have you ever had to fork over the right to pull your credit score for either rent or (in some limited cases) to apply for a job? Not being able to understand: a) Why you were rejected. b) Why you are paying a higher interest rate. c) What you can do to fix that?

Housing and employment is fundamentally a question of human rights, not just banking profitability or academic algorithm improvements.

Part of the reason why Credit Scoring (which is intrinsically algorithmic decision-making) is because it replaced the "old boys" network that used to dominate. In this world you went to your banker, and the decision to extend credit was dominated by your personal relationship with a banker who might not be the same ethnicity, religion, nationality, etc as you would dominate your ability to purchase housing. The credit score democratized financial access.

It can still be used to discriminate. There have been a number of studies that have shown that simply being from a white community tends to increase the number of other people you can lean on in a emergency, and hence make you less likely to default on a loan. From a pure predictability point of view, a bank is at a lower risk of default lending to someone from that community, but that in turn denies financial access to people not in that community, continuing the trend.

A big problem in this kind of financial model is that it's relatively easy to accidentally or deliberately find other variables that mirror "protected" vectors. A simplistic example is zip code, where the zip code reflects a area that is predominately ethnic minorities.

So it's not cut and dried. It's my PoV that It's not predictability versus red-tape, and the people trying to do unaccountable analytics in this space are (perhaps in-advertedily) perpetuating racism.

<comment><USER4> I think the case of zip code based discrimination is even named. It's called redlining, and is being used in machine learning world to describe indirect discrimination based on certain attributes (eg. discriminate people based on their zip code, a zip code with mostly black population for example)

<comment><USER5> But it's also very important to understand _why_ machine learning systems do this. If you take race neutral data, and run virtually any machine learning system on it, you'll get a race neutral output. I.e., if a $40k/year black person in 10001 is equally likely to default as a $40k/year white person in 10002, then the algorithm can be expected to give them equal credit scores.

In the event that an algorithm choose black zip codes for extra penalties, it's because the algorithm has deduced that _stereotypes are correct_ \- the bank will make more money not lending to blacks at given rates, because something directly correlated with race and not captured by fields like income/past defaults/etc is predictive.

I discuss this in greater detail here, taking linear regression as a toy example: [https://www.chrisstucchio.com/blog/2016/alien_intelligences_...](https://www.chrisstucchio.com/blog/2016/alien_intelligences_and_discriminatory_algorithms.html)

Having consulted for folks doing lending, I'll mention an insanely difficult $100B startup idea here: build an accurate algorithm that exhibits no disparate impact. The amount of money you'll save banks is staggering.

<comment><USER3> True, and then the question becomes, is the stereotypes correct, or is it that the credit score is then propagating a system that enforces this outcome.

<comment><USER6> The stereotype (That predominantly black households in a poor area code are poorer then the predominantly white households in a rich area code) can be proven correct by simple demographics, and the fact that household wealth is correlated with credit score.

The problem is that there's a positive feedback loop at play, here.

<comment><USER7> The bank already knows their assets and income. The question is if an equally poor and educated white is just as likely to repay a loan as an equally poor and educated black. I imagine most of the difference would go away. Unless you really believe black people are inherently less likely to pay back loans, all else equal.

<comment><USER5> If you can come up with an algorithm that reproduces this conclusion - <USER2> accuracy even remotely close to the "racist" ones - banks will cross heaven and earth to pay you $billions.

Unfortunately the only public analysis I'm aware of is from a blog using Zillow data: [https://randomcriticalanalysis.wordpress.com/2015/11/22/on-t...](https://randomcriticalanalysis.wordpress.com/2015/11/22/on- the-relationship-between-negative-home-owner-equity-and-racial- demographics/#my_analysis)

This effect is reproduced in various walks of life, e.g. education.

You'll be doing social good in addition to earning billions of dollars. What's not to like?

<comment><USER7> I do in fact have an algorithm to remove racism from models, but I doubt its worth "$billions". The whole point of my argument is that it shouldn't be necessary. Surely you don't really believe racist stereotypes are true?

<comment><USER5> I do, in fact. There is extensive research supporting the fact that many (though not all) are accurate. The typical racist stereotype is about twice as likely to replicate as the typical sociology paper.

Here are a couple of review articles to get you started: [http://www.spsp.org/blog/stereotype-accuracy- response](http://www.spsp.org/blog/stereotype-accuracy-response) [http://emilkirkegaard.dk/en/wp-content/uploads/Jussim-et- al-...](http://emilkirkegaard.dk/en/wp-content/uploads/Jussim-et-al- unbearable-accuracy-of-stereotypes.pdf)

I didn't say that simply removing "racism" (by which I assume you mean disparate impact) is worth billions. I said doing so with the same accuracy as models which are "racist" is worth billions. Obviously you can do anything you want if you give up accuracy.

Why do you believe they are false? Simply because it's socially unacceptable to think otherwise?

<comment><USER8> > _Simply because it 's socially unacceptable to think otherwise?_

Please stop insinuating this at people in HN comments. You've done it frequently, and it's rude. (That goes for "simply because of your mood affiliation?", etc., too.)

<comment><USER5> Dang, I'm really confused here. As Houshalter points out, he was deliberately making an argument based on social unacceptability of my beliefs. Searching for the phrase on hn.algolia.com, I've used the term 5 times on HN ever, never in the manner you imply.

I'm also confused about my use of the term "mood affiliation". Searching my use of the term, most of the time I use it to refer to an external source with a data table/graph that supports a claim I make, but a tone which contradicts mine. For example, I might cite Piketty's book which claims the best way to grow the economy is to funnel money to the rich (this follows directly from his claims that rich people have higher r than non-rich people). What's the dang-approved way to point this out?

In the rare occasion I use it to refer to a comment on HN, I'm usually asking whether I or another party are disagreeing merely at DH2 Tone (as per Paul Graham's levels of disagreement) or some higher level. What's the dang- approved way to ask this?

[http://paulgraham.com/disagree.html](http://paulgraham.com/disagree.html)

Let me know, I'll do exactly as you ask.

<comment><USER8> > _usually asking whether I or another party are disagreeing merely at DH2 > Tone (as per Paul Graham 's levels of disagreement) or some higher level. > What's the dang-approved way to ask this?_

That seems like a good way to ask it right there.

Possibly I misread you in this case (possibly even in every case!) but my sense is frequently that you are not asking someone a question so much as implying that they have no serious concern, only emotional baggage that they're irrationally unwilling to let go of. That's a form of derision, and it doesn't lead to better arguments.

But if I've gotten you completely wrong, I'd be happy to see it and apologize.

</comment></comment></comment></comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER4> I made a couple of experiments on discrimination free machine learning models with naive Bayes, and I changed my perspective on data science.

Usually people are concerned about maximising prediction accuracy, and never stop to think about what correlations is the model finding down below, and the human biases present in the data annotations.

Removing sensitive variables (gender, race, etc) doesn't always help, and specially if the models have high impact on people's lives (applying to loans, university, scholarship, insurance), we cannot afford to blindly use existing annotations in black box models.

All of this excludes the fact that companies will probably maximise to profit, and will use "algorithm" as an excuse to turn down people disregarding ethics.

Edit: here is code for the curious [https://github.com/sergioisidoro/aequalis](https://github.com/sergioisidoro/aequalis)

<comment><USER9> > Usually people are concerned about maximising prediction accuracy, and never > stop to think about what correlations is the model finding down below, and > the human biases present in the data annotations.

Because maximizing prediction accuracy is inherently unbiased. Bias is when the predictions made are _inaccurate_ to the detriment of a group of people. If you had a prediction algorithm that functioned using time travel to tell you with 100.0% accuracy who would pay back their loans, there would be a racial disparity in the result, but the cause of it is not the fault of the algorithm.

And you can't fix it there because that's not where the problem is.

Suppose you have a group of 800 white middle managers, 100 white sales clerks and 100 black sales clerks. Clearly the algorithm is going to have a racial disparity in outcome if the middle managers are at 500% of the cutoff and the sales clerks are right on the line, because it will accept all of the middle managers and half of the sales clerks which means it will accept >94% of the white applicants and 50% of the black applicants.

But the source of the disparity is that black people are underrepresented as middle managers and overrepresented as sales clerks. The algorithm is just telling you that. It can't change it.

And _inserting_ bias into the algorithm to "balance" the outcome doesn't actually do that, all you're doing is _creating_ bias against white sales clerks who had previously been on the same footing as black sales clerks. The white middle managers will be unaffected because they're sufficiently far above the cutoff that the change doesn't affect them, even though that they're the source of the imbalance.

<comment><USER10> You entirely miss the point! The point is that in supervised learning for example, if you optimize prediction accuracy with respect to your human generated examples, you will get a model that exactly reproduces the racist judgment of the human that generated your training set.

<comment><USER9> > The point is that in supervised learning for example, if you optimize > prediction accuracy with respect to your human generated examples, you will > get a model that exactly reproduces the racist judgment of the human that > generated your training set.

In which case you _aren 't_ optimizing prediction accuracy. Prediction accuracy is measured by whether the predictions are true. If you have bias in the predictions which doesn't exist in the actual outcomes then there is money to be made by eliminating it.

It seems like the strangest place to put an objection where the profit motive is directly aligned with the desired behavior.

<comment><USER11> You need to think about how we measure truth and even what truth is.

In machine learning we tend to assume the annotations and labels are "true" and build a system towards that version of the "truth".

> Prediction accuracy is measured by whether the predictions are true.

The more I think about this sentence, the less sense it makes. Prediction accuracy can only be measured against records of something, and that record will be a distortion and simplification of reality.

<comment><USER9> > Prediction accuracy can only be measured against records of something, and > that record will be a distortion and simplification of reality.

Prediction accuracy can be measured against _what actually happens_. If the algorithm says that 5% of people like Bob will default and you give loans to people like Bob and 7% of them default then the algorithm is off by 2%.

<comment><USER11> You are still assuming everything that is recorded to be "like Bob" is the truth and captures reality clearly.

Moreover you need to give loans to everybody in order to check the accuracy of the algorithm. You can't just check a non random subset and expect to get non biased results.

<comment><USER9> > You are still assuming everything that is recorded to be "like Bob" is the > truth and captures reality clearly.

Nope, just finding correlations between "records say Bob has bit 24 set" and "Bob paid his loans." The data could say that Bob is a pink space alien from Andromeda and the algorithm can still do something useful. Because if the data is completely random then it will determine that that field is independent from whether Bob will pay his loans and ignore it, but if it correlates with paying back loans then it has predictive power. The fact that you're really measuring something other than what you thought you were doesn't change that.

> Moreover you need to give loans to everybody in order to check the accuracy > of the algorithm. You can't just check a non random subset and expect to get > non biased results.

What you _can_ do is give loans to a random subset of the people you wouldn't have to see what happens.

But even that isn't usually necessary, because in reality there isn't a huge cliff right at the point where you decide whether to give the loan or not, and different variables will place on opposite sides of the decision. There will be people you decide to give the loan to even though their income was on the low side because their repayment history was very good. If more of those people than expected repay their loans then you know that repayment history is a stronger predictor than expected and income is a weaker one, and if fewer then the opposite.

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER7> Is my reading of this law correct? It sounds like it outlaws all use of algorithms to evaluate people. The right to explanation is just for the rare exceptions where a "member state" authorizes it. But the legalese is difficult to parse, and no one else seems to get this impression.

I think this is really bad. That's the majority of uses of machine learning. It also has a lot of economic value to predict things like how likely someone is to pay back a loan, or even who is a spammer.

Most importantly, most these applications will go back to human judgement. And humans are _far worse_. Human predictions about candidates are really bad. We are incredibly biased by things like race, gender, and especially attractiveness. Unattractive people get twice the sentences of attractive people. Not to mention random stuff, like people being judged more harshly when the judge is hungry before lunch. They also rarely give explanations for their decisions, if they are even aware of the true reasons for them.

Going back to humans is a huge step backwards and will hurt a lot more people than it helps. I think the same regulations that apply to algorithms, should apply to humans. That would show the absurdity of these laws. But humans are algorithms after all. And particularly bad ones (for this purpose anyway.)

<comment><USER12> > It sounds like it outlaws all use of algorithms to evaluate people. The > right to explanation is just for the rare exceptions where a "member state" > authorizes it.

Who says that this is rare? That's a pretty standard way EU regulations are written to mean "Action A can only be allowed if conditions B are met".

If the regulation is approved, member states take a look and change their laws accordingly. They could outlaw algorithmic decision making if they wanted to (but they wouldn't need that regulation to do that). Much more likely is that they impose those conditions on the companies that use such algorithms just like they already impose a lot of conditions (such as "ethnicity is not allowed to be a direct input variable" currently is).

<comment><USER13> > If the regulation is approved, member states take a look and change their > laws accordingly.

Small nitpick id I may: you seem to be confusing Regulations with Directives, which must get transcribed within a certain timeframe in national law. Regulations apply directly - no transcription needed.

Also: either way, EU law has primacy over national law.

<comment><USER12> Thanks for pointing this out, my post was not entirely clear on that matter. This proposed regulation does in fact contain some directive-like clauses with regards to the issue at hand. In this specific case they require the member states to disallow certain practices in their national law.

It's also not uncommon for member states to enact laws dealing with the effects of a (directly applicable) regulation.

</comment></comment></comment></comment><comment><USER14> The United States does not have anything as sweeping as this, but in the limited area of credit the Equal Credit Opportunity Act (ECOA) requires that lenders that turn down your credit application give you an explanation. From the Federal Trade Commission's site:

      The creditor must tell you the specific
      reason for the rejection or that you are
      entitled to learn the reason if you ask
      within 60 days. An acceptable reason might
      be: "your income was too low" or "you haven't
      been employed long enough." An unacceptable
      reason might be "you didn't meet our minimum
      standards." That information isn't specific
      enough.

[https://www.consumer.ftc.gov/articles/0347-your-equal- credit...](https://www.consumer.ftc.gov/articles/0347-your-equal-credit- opportunity-rights)

(The ECOA is at 15 U.S. Code § 1691, for those who want to go find the gory details)

In one of the lectures from Caltech's "Learning from Data" MOOC the professor mentioned that problem came up at a major lender that he had consulted for. He did not say how they solved the problem.

I wondered if you could generate a satisfactory rejection reason, at least as far as the law is concerned, by taking rejected applications and running them through the system again but with some of the inputs tweaked until they get an acceptance. Then report that the rejection was due to the un-tweaked parameter value.

For instance, if you picked income to tweak, you'd raise the income by, say, $5000 and try again. If that is rejected, raise it another $5000. If it then passes, you can tell the applicant they were rejected because their income was too low, and that an additional $10000 income would be enough to qualify them for a loan.

You'd have to put a bit of sophistication into this to make the explanations reasonable. For instance, if a rejected application would be approved with a very large tweak to income or with a small tweak to employment length it would probably be better to give employment length as the reason for rejection.

<comment><USER15> A potential problem with this is that if you "tell the applicant they were rejected because their income was too low" then if this is disputed then they will likely be able to point to many other applicants with even lower income that were accepted, because their total combination of factors was better.

A simple explanation "factor X is too low" implies that there exists a particular cutoff that is required and sufficient; but an explanation that _accurately_ describes why you were rejected would likely be too complex to be understandable.

But yes, probably the intended result could/should be simply identifying the top 1-2 factors that dragged your score down compared to the average accepted candidate (which can be rather easily done automatically even for the most black-box ML methods) and naming them appropriately.

<comment><USER16> > an explanation that accurately describes why you were rejected would likely > be too complex to be understandable.

This is the core of the problem. Have you ever tried to explain a statistical segmentation to someone? It requires them to _let go_ of how they think about if-x-then-y criteria and attributes. Very intelligent people who understand statistics, weighting, clustering, etc. struggle with it--think about an "average" person.

You can back into a reason: "Here's the attribute(s) on which you deviate the most from the median of the segment of which you want to be a part." But it's not a linear, single-variable "fix".

</comment></comment></comment><comment><USER17> Good luck arguing the 'why' when you have a trained neural network and all you have is the network and weights.

On a more serious note, I love transparency but again, this is an overeager regulation (not surprising from the EU). You almost never get the true reasons for being rejected, be it in an interview or for a credit, etc. You have to figure that out yourself from the often very vague rejection letter. Our minds are basically running algorithms to which we have even less insight than our computer algorithms. Therefore, this law only hampers the flourishing of the economy while providing no value whatsoever.

Another law that 'solves' a non-issue, brought into being by overpaid career politicians.

<comment><USER18> If all you have is a network and weights, you have a black box. While it might be magic and make the _right_ decision most of the time, we need to look in and see why those decisions are being made, somehow.

This generally basically means seeing how the net was trained, and its initial conditions. If you threw away your training methodology and data set, why should anyone trust that your algorithm can make suitable decisions? Are we to assume that nobody who writes AIs which are making influential decisions has vested interests?

If you look for example at common law; we have a system where decisions by judges are binding, and future equivalent cases are required to uphold the previous decisions. The only way this system can work of course, is if someone is keeping a record of the previous decisions. The alternative is we'd have a system of judgement based on heresay, and we can have about as much confidence in it as asking a random person on the street to make the decision.

The technical challenge is really about storage and retreival. If we know that discarding training data makes our neural networks "unaccountable for their decisions", then our technical requirement must be that we store the training data _in its entirity_ , such that we can look back and maybe glimpse at why a neural network may behave as it has.

For example, I might create an AI which is used to decide whether to give someone a mortgage. I could have millions of samples as training data, but I might chose to sort the training data by race, and begin training a network initially so that non-white people who have been rejected mortgages initially overtrain the network to correlate race with rejection, then use a limited sample of white man, mostly accepted mortgage requests to train it the other way.

Of course this is extreme, and an expert who looked at such data set would quickly notice that the AI is unfit for purpose, and blatantly racist. But he needs the training set to even have a chance of concluding that. Without it, he has effectively random numbers which tell him next to nothing.

These laws aren't meant to stifle innovation or economic benefits, but only to ensure that fair treatment is practiced in their development. As far as I see it, if you have a neural network, a sound justification of its design and the methodology you used, combined with a complete data set and training set which can be analysed for biases, then there's no reason these regulations should get in your way.

</comment></comment><comment><USER13> Trying to picture the "right to explanation" being applied to a Google car or an automated vacuum cleaner. Mm...

In more seriousness, would it even be feasible for recent machine learning algorithms to explain _why_ they opted for a decision?

<comment><USER19> I suppose we can always weasel around with the definition of "why".

 _Why_ was my claim refused?

Because you have a large number of consonants in your surname which, when combined with the fact your phone number has a prime number of "4" digits, leads to an increase risk of fraud.

 _Why_ do those factors indicate fraud?

Because our training data indicates that they do.

 _Why_ does your training data indicate that?

...

<comment><USER15> Well, that's not a problem - when you say "my training data indicates that they do" what you really mean is "our previous experience shows that applicants with these factors have a significantly elevated chance of not paying back our money", which by itself is a valid reason as it is objective and based on real world facts.

A bigger problem would be when your features actually turn out to be proxies for 'undesired' features. For example, if you do put names in as features, then any credit risk system _will_ learn that e.g. "Tyrone Washington" is a higher risk customer (assuming everything else is equal) than "Eric Shmidt", since those names are highly predictive of most socioeconomic factors; however, you'll risk a judge saying that this implements racial discrimination even if you exclude race directly, since those names are even more predictive of skin color than income.

<comment><USER20> Kind of, mostly. If the training data is correlating consonants in surname or prime number of '4' digits in a telephone number with increased credit risk, there's a really solid case there to ask where the training data sample came from and check it for biased sourcing.

<comment><USER15> If the system is correlating consonants in surname or prime number of '4' digits in a telephone number with increased risk, well, then in practice that simply means that you have a bad system with a serious overfitting problem - i.e., not a problem with your data but a problem with your learning process that's obtained "superstition" by treating random noise as important signal.

However, if you do some reason analysis, then this is useful and gives a lot of interesting information.

Names will be correlated with socioeconomic status and ethnicity, and also with education level [of your parents], income, etc.

Addresses obviously are strong indicators especially if you combine them with data about that location or even particular building if that's available.

Even phone numbers can carry a lot of true information e.g. location, or for countries that handed out new ranges for mobile phones, it would be correlated with how long you've had your phone number/how often you change them, which is a proxy for stability.

I'd bet that you could even build a somewhat predictive model of credit risk based on character-by-character analysis of email adresses - even excluding domain names, the fact if someone chose fluffybunny420 or jonathan.cumbersnatch carries _some_ signal; not very much, but if you don't have better data then it will serve as a vague proxy for age, lifestyle and frivolousness (and in case of domain names, possibly employer) which all are important influencers of risk.

<comment><USER20> This explanation is solid and speaks loudly to the benefits of right to explanation. Because if the explanation is "We've found signal in the data that correlates strongly with social biases (example: surname analysis---of course there's increased risk, if the lender is operating in a country where people don't trust anyone whose name starts with "Mc", for example), and we can use that signal to basically ethnically profile without having to admit that we're ethnically profiling..." that's bad, and the law may address it.

"Redlining" practices aren't bad because they don't economically work. They do economically work; if one demographic is societally disadvantaged, you don't do your firm any short-run favors by catering to them, because they aren't where the money is. They're bad because they work by unfairly shifting the pie away from people who don't actually deserve to have the pie shifted away from them---it's a variant on "sins of the fathers" reasoning to bias on categories like surname, or race, or demographics that correlate strongly with those demographics (like geography, in cities in the United States). There are moral reasons to not allow those practices to get codified into algorithms so the people using them can excuse bad behavior as "just following the computer's orders."

</comment></comment></comment></comment></comment></comment><comment><USER21> The first thing that came to my mind was... what about advertising on the web ? This is an area where machine learning and algorithmic decision-making use discrimination to either show or not an ad to a particular user. There are millions decision made everyday. Under that regulation, could you ask for the reason an ad was shown to you ?

<comment><USER22> Nope, because advertisement doesn't "significantly affect" you. At least that's how it's meant and how it will be understood. This is about credit, employment, insurance, medical care etc.

<comment><USER23> Medical care?! Oh God. Well, I suppose the AMA will keep the US from embracing machine learning so it's not like this is actually a chance for the US to reverse its lag in mortality statistics.

</comment></comment></comment><comment><USER24> I think it will be very hard to implement/enforce this regulation. I attended the London AI summit last week, and they had a speaker from a German lender called Kreditech.

There was a question of black box credit scoring, and the speaker made a fair point - their models have 20,000 vectors in determining credit worthiness. How would you begin to break that down to something explainable? You can list the sources of the data, or offer manual review of the application if rejected (which is something they do) - but it would be very hard to show exact causal reasoning patterns in the automatic scoring.

<comment><USER25> >> their models have 20,000 vectors in determining credit worthiness. How would you begin to break that down to something explainable?

Well, somehow they decided that their 20k-parameter model is accurate. They should at least be able to explain why they took that decision, even if the model itself is too complex.

<comment><USER26> Because the training set was covered with x% accuracy. Ok, how does knowing that help?

<comment><USER25> OK, so we're assuming supervised learning. In that case, how were the features of the training set chosen? And how was the training set labelled?

Most of the time, outside of semi-supervised learning, those things are not in the data, someone has to (painstakingly) decide them. That can very well be explained I believe.

More generally, what I mean to say is that you can learn any number of models from the same data, so to a great extent you choose what your model learns by manipulating your algorithm's (hyper)parameters, the training set's features, choosing what goes into the training set itself etc etc. That process leaves room for a lot of regulatory oversight.

\- in principle. In practice I expect since we're talking about people lending money or selling insurance we're only ever going to get those details out of their cold dead hands, and only with considerable effort at that.

</comment></comment></comment></comment><comment><USER27> Finally I get to know why my mortgage was declined :)

<comment><USER28> >> Finally I get to know why my mortgage was declined :)

My first attempt at one was declined due to "problems with my credit report" or some such. I happened to be working at a place where they did credit checks on customers, so I asked someone to pull my credit report. They had all my stuff, but had mixed in a bunch of information from someone else with the same name - different age, there were loans on there from when I was 3. I called the reporting company to complain and they kept getting caught up in "how did you get your report?" as if I had done something wrong in just having access to their incorrect information. Perhaps having someone pull the info instead of the "consumer" getting it through proper channels was against some rule - this was 1995. I think the same is often true today, companies want to collect data on you but don't really want you to know what they've got or how they use it.

<comment><USER29> This is exactly why you are legally entitled to receive a copy of your credit report every year. You can challenge anything on it, and have incorrect information removed.

<comment><USER30> When mentioning this, I find it helpful to include this page: [https://www.ftc.gov/faq/consumer-protection/get-my-free- cred...](https://www.ftc.gov/faq/consumer-protection/get-my-free-credit- report)

If you are one of the people just finding out about this today, and you Google "free credit report", you come up with a _loooot_ of bad, scammy links. This is how you get your legally-mandated, really free, annual credit report, starting from an ftc.gov address so you know it's really the right one.

<comment><USER31> Credit Karma is legit and free. Also gives you your credit score and credit monitoring (all free).

(Disclaimer: I work there).

<comment><USER29> To be fair, the credit score (VantageScore 3) you provide is not the credit score (FICO) most people are interested in, and that is used for most credit decisions.

</comment></comment></comment></comment></comment></comment><comment><USER23> I wonder machine learning could just handle this the way that humans do. Human decisions processes are of course extremely opaque even if we're good at coming up with post-hoc justifications for our reasoning as needed.

"You're income is too low" "But his income is also low and he got a loan!" "Well he has a better credit score." etc

It would obviously be impossible to give a complete and accurate explanation of a machine learning decision process the same way it would be impossible for a human learning decision process, but I wonder if it would actually be difficult to come up with particularly salient distinguishing features in general or between certain cases the same way that people do?

</comment><comment><USER32> Can't you just say the decision was based on a random forest trained on the following parameters with historical data. You could get more specific on how the model was trained or even show model if you have to. I'm sure you can create a small-print tree diagram.

In terms of human review, does this require more than a spot check? I can imagine look over the p-value and possibly spot check the input data, and then approve it.

</comment><comment><USER33> hi, I'm one of the co-authors. This feedback is awesome. I have a longer version in the works. if you're interested please feel free to email me, bryce dot goodman at stx dot ox dot ac dot uk.

Thanks! Bryce

</comment><comment><USER34> So what effect will this have on risk assessment as used by insurance companies?

"Insurance companies use a methodology called risk assessment to calculate premium rates for policyholders. Using software that computes a predetermined algorithm, insurance underwriters gauge the risk that you may file a claim against your policy. These algorithms are based on key indicators about you and then measured against a data set to weigh risk. Insurance underwriters carefully balance the insurance company's profitability with your potential need to use the policy."

</comment><comment><USER35> > We argue that while this law will pose large challenges for industry, it > highlights opportunities for machine learning researchers to take the lead > in designing algorithms and evaluation frameworks which avoid > discrimination.

This sounds like a very slippery slope.

Let me get this straight, what the EU wants to do here. If I make a business that decides how to, say, make loans to people, and I have software that analyzes how they filled out an application or took a written/logical test or _something_ I may have to:

1) Disclose the findings and methodology of my proprietary algorithm

2) Potentially undo decisions that are deemed discriminatory

3) Thereby potentially be forced to choose who I do and do not do business<USER2>
Some regulations are good, they help us avoid things like moral hazards. Some regulations, however, act more like power grabs that take away freedom from individuals and businesses.

I'm sorry to say but this sort of thing sounds like the latter.

There are some uncomfortable facts about people and society that algorithms will uncover, and that people will try using to avoid making mistakes. Here's a poignant example, albeit with much less sophistication: [http://www.nytimes.com/2015/10/31/nyregion/hudson-city- bank-...](http://www.nytimes.com/2015/10/31/nyregion/hudson-city-bank- settlement.html)

> Instead, some officials say, some banks have quietly institutionalized bias > in their operations, deliberately placing branches, brokers and mortgage > services outside minority communities, even as other banks find and serve > borrowers in those neighbourhoods.

Alright sure. I think we all know it's common sense to not open a jewellery store in inner city Chicago. The local clientele would not be able to afford such products and services, and that's enough of an argument, it's probably business school 101. Just like how you're not going to find a good market for a food delivery startup in Middletown, USA. The economics probably don't work. EDIT - and if you think these businesses are wrong, and they should be in these communities, let their competitors eat their lunch!

But it's viewed as discriminatory. Back to the paper, it's discussing essentially the same thing: it's the natural evolution of "don't start a jewellery store in inner city Chicago" turning into "don't start a jewellery store in neighbourhood where access to urban center is less than X, available nearby 3-lane highways is less than Y, percent of women from 18-29 is less than Z, proportion of people from culture W who are in the bottom quartile of people who buy jewellery is more than V, ...".

The solution here isn't to regulate and force people to open jewellery stores in inner city Chicago. The solution, if we really want to bring diamonds to Englewood, is to drastically reduce the poverty and crime rates so that businesses will _want_ to sell there ([https://en.wikipedia.org/wiki/Englewood,_Chicago#Socioeconom...](https://en.wikipedia.org/wiki/Englewood,_Chicago#Socioeconomics)).

And if you think having access to a local business is a "right", then you must concede that such a public good should be provided by a central government of some type. And then you have to ask yourself, do you want the government managing your bank loans, or would you rather have private companies compete on prices and service? If you want the latter, then you need to accept that certain things are just going to arise (or not arise) naturally.</comment>


Facebook deletes Social Fixer page, blocks page admins - <AUTHOR> http://socialfixer.com/blog/2013/09/12/beware-your-business-is-at-the-mercy-of-facebook-social-fixer-page-deleted-without-explanation/ <comment><USER2> One of the most important lessons I've learned during the growing-up phase of my being an entrepreneur -- and one of the first things I'm doing with my next startup -- is putting together a list of "doomsday scenarios" right off the bat.

What are the _single_ actions / events / scenarios that could kill your business overnight?

I emphasize single because every business is going to have the potential for a series of unforeseen events kill them (i.e. Microsoft would probably not have seen 'Apple releases a phone' as a business/Balmer-killer), but every business should be keenly aware of the single-points-of-failure (SPoF).

It seems obvious, but so many folks either willfully ignore or choose to pretend that SPoF don't exist or rationalize the scenario away.

Examples:

* You run on AWS in a single availability zone, and that availability zone goes down for a week ("The cloud is super reliable. Amazon won't go down.")

* Your CTO is the only person on the company who knows the product, and he quits overnight / gets injured / becomes incapacitated. ("He's my partner. He'd never abandon the business. We're in this together.")

* You rely solely on Facebook / Twitter / Apple App Store and are at the mercy of their changes in policy ("Oh Facebook would never just shut us down.")

* You're built solely on the Foursquare Places API. ("Foursquare won't just kill all the companies that rely on their API...right?")

Etc. etc. etc.

Now, this isn't saying that you should be paralyzed by the potential for SPoFs to hurt your business -- it may make sense to proceed even with them around (i.e. lots of people have made a lot of money building FB apps).

But you MUST be aware of what they are, and you MUST have a plan in place to handle the continuation of the business if one of them comes true.

<comment><USER3> Agreed. But also note - this isn't a point of failure. It's an annoyance, and it's Facebook being arbitrary and inconsistent. But it doesn't affect my ability to deliver the product. It just makes it a little harder, because the most logical communication medium between myself and my users has been taken away.

<comment><USER4> Of all the companies out there, Facebook is the least arbitrary and inconsistent I know! Just imagine they're a perfectly rational, self- interested actor who plays realpolitik all the time and plan accordingly.

For example, how does FB benefit from your product? Does it hamper them in any way? Even the name (Social "Fixer") does not align with FB's interests.

The rational thing to do would be to shut you down. If you had leverage or tied into a broader strategy they might not. But you don't, so they did.

<comment><USER3> Agreed. Unfortunately, this is a personal side project. If it were my business, I would certainly approach it differently. The only benefit they get is that lots of people continue using Facebook who otherwise may have given up on it. Oh, and they sometimes seem to add features that I come up with first. But in general, yes - I am someone they would like to just make go away.

Which is fine - they have a right to do that. But everyone should be aware of this, because at some point when Facebook decides they don't like your politics, or hobby, or sexual choices, they'll arbitrarily shut you down, too. So, don't give them any more than you can afford to lose. But I see many companies investing a ton of time and money into their Facebook strategy. My point is to beware, because it may not be a good choice.

<comment><USER4> The main aspect of your product they didn't like us that you were inserting yourself between Facebook and their users. Any platform provider would try to stop that because that relationship is the foundation for every platform's value.

The only possible chance you have around this is growing so fast and becoming so essential that the platform provider can't punish you without punishing themselves. See, e.g., Facebook and Zynga.

</comment></comment></comment></comment></comment><comment><USER5> It is also against Facebook TOS to create or use browser extensions to alter the Facebook experience. This has been the rule for many years. The fact that this page and extension did not get banned sooner is the real story.

<comment><USER6> They can write that in their terms & conditions all they like, but it doesn't make it legal or enforceable.

<comment><USER7> How would it be illegal to remove pages from their own platform that alter Facebook's appearance? As far as enforceability, that seems demonstrably false based on this post.

<comment><USER6> Um, I never said that anybody did anything illegal. In fact, that was the central point of my comment.

SF-guy can write his app, I can use it, and FB can delete his FB pages. No laws broken.

<comment><USER7> >They can write that in their terms & conditions all they like, but it doesn't make it legal or enforceable

That was In response to "It is against Facebook tos to create or use extensions that alter the experience".

When you say "that doesn't make it legal", does that not imply you think something is illegal....? Regardless, I see from your comment that you weren't talking about the page removal, but rather going on a tangent about the legality / enforceability of the ban on creating and using extensions that alter Facebook (as opposed to banning pages n Facebook that promote these extensions). We're on total agreement on that.

</comment></comment></comment></comment></comment><comment><USER4> This has been Facebook's MO for almost 5 years now. What Social Fixer was doing in particular is among a class of applications that Facebook has always deleted or banned as soon as they noticed them. I've had applications with 5-10MM MAU deleted without warning and know dozens of folks with similar stories.

That's life developing for Facebook. Don't be under any illusions if you decide to play in their sandbox.

</comment><comment><USER8> "my Page had 338,050 Likes, my Support Group had 13,360 members, and my Interest List had 1.47 Million followers. But all of that work was wiped out in an instant when Facebook decided to shut it down without notice."

"Not only did they remove the page, but they also blocked my personal account from posting anything for 12 hours (I can't even Like anything). They also did the same for anyone who was an Administrator or Moderator of the Page – including my wife's personal account! Members of the support team, who generously volunteer their time to help users, have been shut out as well. They did one big sweep, I guess."

I came here to post this story, it is unfortunate that this news is not getting more visibility. If possible, check "tell facebook what you think" and try to help Matt since he has worked very hard on this app

<comment><USER9> It is against the Facebook TOS, unfortunately shouldn't we be telling him "don't violate Facebook's TOS without expecting repercussions?"

<comment><USER10> The TOS is not above criticism or complaint, and I for one am tired of people being shouted down because they dared take issue with draconian policies.

<comment><USER7> I tend to have a lot of problems with the way Facebook operates (mostly involving how they treat their users), but they seem pretty reasonable in this case. How is it draconian to not allow a Page on your platform that (among other things) blocks your source of revenue? (social fixer allows blocking Facebook ads)

<comment><USER10> So does Adblock Plus, so does Noscript, so do the content filtering features of major browsers, yet those all have major pages. Blocking the people is just plain dickery on Facebook's part.

<comment><USER9> Not at all. FBpurity is still functioning. I have to download it from a 3rd party site. You can't expect Facebook to allow products which deliberately violate their TOS to be hosted/promoted/advertised _on Facebook_.

<comment><USER10> >You can't expect Facebook to allow products which deliberately violate their TOS to be hosted/promoted/advertised on Facebook.

I certainly can (and do) expect humanity and rational thought to take priority over cold application of _The Rules Über Alles_ \- and any instances of the latter deserve to be called out for the silliness that they are.

Further, it's not like Facebook didn't know about this until now. _Social Fixer_ used to be called _Better Facebook_ until Facebook tapped them on the shoulder and asked them to change the name last year.

<comment><USER9> FB, Google, Amazon etc aren't humanitarian organizations or charity. They are in the business of providing a product, and making money while doing it.

<comment><USER10> Which has what to do with the price of tea in china?

<comment><USER9> It has to do with the fact that they have no reason to "play nice for the betterment of humanity" if you don't follow their rules, and every reason to be well within their rights to boot you from their site for what ever reason they choose.

</comment></comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER11> _sigh_ , all these "hackers" confused about the difference between a facebook app and a browser extension, confused about the difference between the app and the facebook page about the app ...

These comments are depressing as hell.

On topic: I'd just be happy you lasted this long with a facebook page for an extension that does this kind of thing. This gets you an extra bump in publicity and now you don't have the threat of this hanging over your head anymore. Is there anything that your users want that would _really_ piss Facebook off that you can do now but were wary of doing before because of not wanting to push them too far? Aggressive ad filtering?

</comment><comment><USER12> This is why I'm so scared / hesitant to build something that depends entirely or mostly on a third party company for its success or even ability to exist. Businesses that depend on Facebook or Twitter or Apple's App Store or even Meetup are scary. I have businesses that depend on one or more of these. And I'm scared. I'm doing my best to extricate myself from this dependency and build substantial visibility elsewhere where I can.

</comment><comment><USER13> From the FAQ:

"Can Social Fixer hide the Sponsored ads?

Yes, just click on the "X" in the upper right of the box, and all boxes with the same title will be hidden every time you load a page."

There is a lesson here somewhere...

<comment><USER14> Does that mean [https://www.facebook.com/adblockplus](https://www.facebook.com/adblockplus) is next?

<comment><USER15> AdBlock Plus doesn't block Facebook ads for me. Am I using it wrong?

<comment><USER16> By default, Adblock Plus "Allow[s] some non-intrusive advertising."

You have to go into the settings and opt out to stop all ads.

</comment></comment></comment></comment><comment><USER17> _Beware: Your Business Is At The Mercy Of Facebook!_

...if you allow yourself to rely entirely on them.

<comment><USER18> ...and violate their TOS at the same time.

<comment><USER19> Well, whether you do or not, if it's the sole platform you build on, you're at their mercy.

Violating the TOS means you should expect to get burned (and you've done it to yourself). Either they utterly failed at any kind of due diligence while designing this product or they're lying about not understanding Facebook's actions; neither really engenders sympathy.

I'm not a fan of FB, but this clearly is not some arbitrary evilness on their end.

<comment><USER3> I did not violate their TOS. This is not a Facebook App that uses their API, therefore I am not bound by their developer TOS.

<comment><USER20> You are bound by their whims, however. I feel for you-- it's unfortunate to have something you put your heart and soul into shut down without notice.

This is why I would hesitate before developing a facebook or iOS app. Being subject to the whims of some tech bureaucrat's interpretation is a little too discomforting.

<comment><USER3> Agree 100%. People have asked for a mobile version of this app, and one of the big reasons I won't do it (or build any mobile app) is because I refuse to invest a lot of time and effort into something that can only be distributed through a 3rd-party channel, with their blessing. I will not take the risk that Apple will not suddenly decide they don't like my app tomorrow. No way.

<comment><USER20> Android doesn't require the same sacrifices and has a larger market share for free apps.

</comment></comment></comment></comment></comment></comment></comment><comment><USER21> I hope one day we can stop relying on rent-seeking third-party platforms for important interactions.

</comment><comment><USER22> Bummer, during election season Social Fixer made facebook usable by blocking any comment with the strings 'republican/democratic/romney/obama.' I am going to miss having the ability to filter crap from my news feed.

<comment><USER23> It's not gone - just being bullied.

</comment></comment><comment><USER24> "My software is a free browser extension that fixes and enhances the Facebook user interface"

I can certainly see why Facebook might not want someone else controlling their user experience. It doesn't seem like too much of a stretch that they would kill this.

</comment><comment><USER25> Facebook trying to align "Community Standards" with their business protectionism and perhaps even be trying to disguise it as a positive thing they're trying to enforce - it's a bit sickening.

</comment><comment><USER26> This is why I wrote this: [http://qbix.com/blog](http://qbix.com/blog)

Don't rely on one platform.

</comment><comment><USER27> I wonder if Facebook will come out with these features soon</comment>


The Analog Computer Museum - <AUTHOR> http://www.analogmuseum.org/english/ <comment><USER2> Did you ever come across the work of Johnathan w Mills and his extended analog computer. He was working on novel analog architectures in the early 2000s. I was a student of his. Sadly he passed away. Here's a link to one of his papers you might find interesting [https://pdfs.semanticscholar.org/eaad/7b8f93265286106c3ce24f...](https://pdfs.semanticscholar.org/eaad/7b8f93265286106c3ce24ff17dd794911674.pdf&sa=U&ved=2ahUKEwjP55-5rZboAhVQCc0KHe4nDIcQFjALegQICBAB&usg=AOvVaw2QoV5AHf3kT4K8zNPGKlrx)

</comment><comment><USER3> Here is a 2h podcast with Ulmann explaining analog computers in great detail:

[https://omegataupodcast.net/159-analog- computers/](https://omegataupodcast.net/159-analog-computers/)

The museum is located in his private home. He will personally show and explain the computers to anyone asking and making an appointment.

</comment><comment><USER4> Forget quantum, 10 years from now all ML accelerators will be analog (mixed signal, to be precise).

<comment><USER5> They probably should be, but I don't know of anyone working on this. Do you have citations?

<comment><USER4> It's still in early stages, but I would keep an eye on Mythic, Syntiant, and Mentium Tech.

</comment></comment></comment><comment><USER6> As far as I can tell, most if not all quantum computing efforts are analog devices at their core. This museum (justly) sees analog computing as a bygone era, but I think we're entering a new heyday.

<comment><USER7> Yes! Have you seen this recent one? [https://www.eurekalert.org/pub_releases/2020-02/tiot- san0226...](https://www.eurekalert.org/pub_releases/2020-02/tiot- san022620.php)

I hope someone builds Von Neumann's harmonic injection computer

<comment><USER8> The world could really do with some harmony injection.

</comment></comment></comment><comment><USER9> Is there a digital playground or emulator for analog computers like these? They look like a lot of fun to play around with

<comment><USER10> VCV Rack can mostly simulate an analog computer. DHE makes some of the most math-related modules. [https://library.vcvrack.com/DHE- Modules](https://library.vcvrack.com/DHE-Modules)

<comment><USER9> thanks, that's exactly what I was looking for</comment></comment></comment>